What is multiple myeloma

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"multiple myeloma"[MeSH Terms] AND treatment

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Multiple Myeloma

Multiple myeloma is a malignant neoplasm of plasma cells (terminally differentiated B lymphocytes) that infiltrate the bone marrow, secrete a monoclonal immunoglobulin (M protein), and destroy bone. It accounts for about 1% of all malignancies and slightly more than 10% of hematologic malignancies in the United States, with roughly 30,000 new cases diagnosed annually.

Epidemiology

Median age at diagnosis: ~65-70 years; only 2% of patients are younger than 40
More common in males than females
Incidence in Black Americans is nearly twice that in White Americans
Environmental exposures (radiation, benzene, organic solvents, herbicides, insecticides) may be contributing factors
Familial clustering has been reported
Goldman-Cecil Medicine, p. 1977; Robbins & Kumar Basic Pathology, p. 420

Pathogenesis

Origin and Precursor State

Almost all cases evolve from monoclonal gammopathy of undetermined significance (MGUS), a premalignant plasma cell condition. MGUS progresses to myeloma at approximately 1% per year. The main molecular events driving progression from MGUS to myeloma are RAS mutations and MYC abnormalities.

Chromosomal Abnormalities

~40% of cases: Primary IgH locus translocations (chromosome 14q32), fusing IgH to oncogenes such as cyclin D1 or cyclin D3 - this drives increased cell proliferation
~40% of cases: Hyperdiploid myeloma with trisomies (without IgH translocations)
~15% of cases: Both IgH translocations and trisomies
Secondary abnormalities (deletion 17p, MYC translocations) arise later and indicate more aggressive disease

Key Growth Factor: IL-6

Interleukin-6 (IL-6), produced mainly by bone marrow stromal fibroblasts and macrophages, is a major growth factor supporting myeloma cell proliferation.

Bone Destruction Mechanism

Myeloma cells cause pure osteolytic disease by:

Upregulating RANKL expression by bone marrow stromal cells, activating osteoclasts
Simultaneously suppressing osteoblasts via IL-3, IL-7, and DKK1
Releasing factors including MIP-1-alpha, SDF-alpha, IL-1 beta, and IL-6

The net result: increased bone resorption (osteoclasts active) without compensatory new bone formation (osteoblasts suppressed).

Goldman-Cecil Medicine, p. 1978; Robbins & Kumar Basic Pathology, p. 420-421

Immunoglobulin Production

The M protein type breakdown:

M Protein Type	Frequency
IgG	52-60%
IgA	20-25%
Light chain only	16%
IgD	2%
Biclonal	2%
Nonsecretory	1-3%

Light chain type: kappa (K) in 65%, lambda (λ) in 35%.

Clinical Features - "CRAB" Criteria

The hallmark clinical manifestations are captured by the acronym CRAB:

Letter	Manifestation	Mechanism
C	Hypercalcemia	Osteoclast activation causing bone resorption
R	Renal insufficiency	Bence Jones protein casts, light chain deposition, hypercalcemia
A	Anemia	Bone marrow infiltration; present in ~75% at diagnosis
B	Bone lesions/pain	Lytic lesions, pathologic fractures

Bone Disease

Lesions are typically "punched-out" defects 1-4 cm in diameter, most commonly affecting the vertebral column, ribs, skull, pelvis, femur, clavicle, and scapula. Pathologic fractures most often occur in the vertebral column and femur.

Renal Disease (Myeloma Kidney)

Bence Jones protein casts obstruct distal convoluted tubules and collecting ducts, surrounded by multinucleate giant cells
Light chain amyloidosis (AL type) deposits in glomeruli
Light chain deposition disease can cause nephrotic syndrome
Hypercalcemia causes dehydration and renal stones
Renal failure occurs in up to 50% of patients and is a leading cause of death

Immunosuppression

Despite elevated total immunoglobulin levels (due to M protein), production of functional antibodies is profoundly depressed. Normal B cells are compromised by mechanisms that remain uncertain. Patients are highly susceptible to bacterial infections, which are a leading cause of death.

Neurologic Complications

Radiculopathy (thoracic or lumbosacral) - the most frequent neurologic complication
Spinal cord compression occurs in up to 10% of patients
Peripheral neuropathy (usually from amyloidosis)
Intracranial plasmacytomas (extensions of skull lesions)

Diagnosis

Laboratory Studies

Serum protein electrophoresis (SPEP): M protein detected in 80% of patients
Serum immunofixation: M protein in 93% of patients
SPEP + urine protein electrophoresis + immunofixation combined: 97% detection
Serum free light chain assay: Convenient alternative to urine studies
CBC: Normocytic normochromic anemia; often leukopenia and thrombocytopenia

Bone Marrow Examination

Plasma cells constitute >10% of nucleated cells in 96% of patients
Myeloma cells are typically: cytoplasmic Ig+, CD38+, CD45-, CD138+, CD56+; only 20% express CD20
Abnormal K/λ ratio (>4:1 for clonal K, or <1:2 for clonal λ) confirms clonality

Imaging

Classic finding: "punched-out" lytic bone lesions on plain X-ray
MRI, CT, and PET scan are used to assess disease extent
PET imaging shows lytic lesions and extramedullary disease, with resolution visible after treatment

Diagnostic Criteria (Goldman-Cecil)

Multiple myeloma is defined by:

≥10% clonal plasma cells on bone marrow examination
M protein in serum or urine (except nonsecretory myeloma)
Evidence of ≥1 myeloma-defining event (end-organ damage: CRAB features, or high-risk biomarkers)

Staging

The Revised International Staging System (R-ISS) incorporates:

Serum beta-2 microglobulin and albumin levels
Specific cytogenetic abnormalities (e.g., del 17p, t(4;14), t(14;16))
Lactate dehydrogenase (LDH)

Adverse cytogenetic features: del(17p), t(4;14), t(14;16), gain/amplification of 1q, del(1p)

Treatment

Smoldering Myeloma

Low/intermediate risk: observation every 3-4 months; no treatment until symptomatic myeloma develops
High-risk smoldering myeloma: lenalidomide (25 mg days 1-21 of 28-day cycle) ± low-dose dexamethasone for ~2 years significantly reduces progression risk and improves overall survival

Active Myeloma - Induction Regimens

Regimen	Abbreviation
Bortezomib + cyclophosphamide + dexamethasone	VCd
Bortezomib + lenalidomide + dexamethasone	VRd
Carfilzomib + lenalidomide + dexamethasone	KRd
Daratumumab + bortezomib + lenalidomide + dexamethasone	DVRd
Daratumumab + lenalidomide + dexamethasone	DRd

Autologous Stem Cell Transplantation (ASCT)

Standard of care after induction for eligible patients
Conditioning: melphalan 200 mg/m²
Transplant mortality: <1%
Stem cells may be collected and cryopreserved, with transplant delayed until first relapse in standard-risk disease - outcomes are comparable to early transplant

Drug Classes

Drug Class	Examples	Mechanism
Proteasome inhibitors	Bortezomib, carfilzomib, ixazomib	Block protein degradation - myeloma cells accumulate misfolded immunoglobulins and undergo apoptosis
Immunomodulatory drugs (IMiDs)	Thalidomide, lenalidomide, pomalidomide	Anti-angiogenic, immune stimulation, direct anti-tumor effects
Anti-CD38 monoclonal antibodies	Daratumumab, isatuximab	Target CD38 expressed on plasma cells
Anti-SLAMF7	Elotuzumab	Targets SLAMF7 on myeloma cells
Corticosteroids	Dexamethasone	Anti-inflammatory, direct cytotoxicity to plasma cells

Newer/Emerging Therapies (2024)

Recent meta-analyses confirm the emerging role of CAR T-cell therapy and bispecific antibodies in relapsed/refractory myeloma (3rd line and beyond). CAR T-cell therapy has shown impressive response rates in heavily pretreated patients. [PMID: 39551604]

Prognosis

Median survival with modern therapy: approximately 5 years; significantly better in younger patients with standard-risk disease
Patients with multiple bony lesions, if untreated, rarely survive more than 6-12 months
"Smoldering myeloma" patients may be asymptomatic for many years
Disease remains incurable in most patients, though responses can be durable

The key causes of death are:

Infections (due to compromised humoral immunity)
Renal failure

Summary Diagram

MGUS → Smoldering Myeloma → Symptomatic Multiple Myeloma
          (1%/year risk)           (CRAB criteria)
                                        ↓
              Bone marrow: ≥10% clonal plasma cells
              M protein: IgG (52%), IgA (21%), light chain (16%)
              Lytic bone lesions → fractures, hypercalcemia
              Bence Jones proteins → renal failure
              Immune suppression → bacterial infections
                                        ↓
                      VRd / DVRd induction → ASCT
                     Proteasome inhibitors + IMiDs + anti-CD38

Sources: Robbins & Kumar Basic Pathology, pp. 420-423; Goldman-Cecil Medicine (International Ed.), pp. 1977-1982

Recent evidence note: A 2024 meta-analysis (PMID: 39551604) found that CAR T-cell therapy and bispecific antibodies have comparable efficacy as 3rd-line therapy for relapsed/refractory myeloma - both represent important advances over traditional salvage regimens.

Create a summary document on myeloma diagnosis and staging I can share with colleagues

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Running Command

mkdir -p /home/daytona/workspace/myeloma-summary && ls /home/daytona/workspace/myeloma-summary/

Writing File

~/myeloma-summary/build_pdf.py

from reportlab.lib.pagesizes import A4
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, KeepTogether
)
from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_JUSTIFY
from reportlab.platypus import PageBreak
from reportlab.lib.colors import HexColor

OUTPUT = "/home/daytona/workspace/myeloma-summary/Multiple_Myeloma_Diagnosis_Staging.pdf"

# ── Colour palette ────────────────────────────────────────────────────────────
NAVY    = HexColor("#1a2e4a")
TEAL    = HexColor("#1a7a8a")
LTBLUE  = HexColor("#e8f4f8")
LTGREY  = HexColor("#f5f5f5")
MIDGREY = HexColor("#cccccc")
WHITE   = colors.white
BLACK   = colors.black
RED     = HexColor("#c0392b")
AMBER   = HexColor("#e67e22")
GREEN   = HexColor("#27ae60")

def make_doc():
    doc = SimpleDocTemplate(
        OUTPUT,
        pagesize=A4,
        leftMargin=2*cm, rightMargin=2*cm,
        topMargin=2.5*cm, bottomMargin=2.5*cm,
        title="Multiple Myeloma – Diagnosis & Staging",
        author="Orris Medical Summary",
        subject="Multiple Myeloma"
    )

    styles = getSampleStyleSheet()

    # Custom paragraph styles
    def S(name, parent='Normal', **kw):
        return ParagraphStyle(name, parent=styles[parent], **kw)

    title_style = S('DocTitle', fontSize=22, textColor=WHITE, alignment=TA_CENTER,
                    fontName='Helvetica-Bold', spaceAfter=4, leading=28)
    subtitle_style = S('DocSub', fontSize=11, textColor=LTBLUE, alignment=TA_CENTER,
                       fontName='Helvetica', spaceAfter=2)
    tagline_style = S('TagLine', fontSize=9, textColor=LTBLUE, alignment=TA_CENTER,
                      fontName='Helvetica-Oblique', spaceAfter=0)

    h1 = S('H1', fontSize=13, textColor=WHITE, fontName='Helvetica-Bold',
           spaceAfter=0, spaceBefore=0, leading=18)
    h2 = S('H2', fontSize=11, textColor=NAVY, fontName='Helvetica-Bold',
           spaceAfter=3, spaceBefore=8, leading=15)
    body = S('Body', fontSize=9.5, textColor=BLACK, fontName='Helvetica',
             spaceAfter=5, leading=14, alignment=TA_JUSTIFY)
    bullet = S('Bullet', fontSize=9.5, textColor=BLACK, fontName='Helvetica',
               spaceAfter=3, leading=13, leftIndent=14, firstLineIndent=-10)
    small = S('Small', fontSize=8, textColor=HexColor("#555555"), fontName='Helvetica-Oblique',
              spaceAfter=3, leading=11)
    table_hdr = S('TblHdr', fontSize=9, textColor=WHITE, fontName='Helvetica-Bold',
                  alignment=TA_CENTER, leading=12)
    table_cell = S('TblCell', fontSize=8.5, textColor=BLACK, fontName='Helvetica',
                   alignment=TA_LEFT, leading=12)
    table_cell_c = S('TblCellC', fontSize=8.5, textColor=BLACK, fontName='Helvetica',
                     alignment=TA_CENTER, leading=12)
    emph = S('Emph', fontSize=9.5, textColor=NAVY, fontName='Helvetica-Bold',
             spaceAfter=5, leading=14)
    note = S('Note', fontSize=8.5, textColor=HexColor("#444444"), fontName='Helvetica-Oblique',
             spaceAfter=4, leading=12, leftIndent=8)

    story = []

    # ── COVER BANNER ─────────────────────────────────────────────────────────
    banner_data = [[Paragraph("MULTIPLE MYELOMA", title_style)],
                   [Paragraph("Diagnosis &amp; Staging – Clinical Reference Summary", subtitle_style)],
                   [Paragraph("For Medical Students &amp; Trainees  |  Based on Goldman-Cecil Medicine &amp; Robbins Pathology", tagline_style)]]
    banner = Table(banner_data, colWidths=[17*cm])
    banner.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), NAVY),
        ('TOPPADDING',    (0,0), (-1,0), 16),
        ('BOTTOMPADDING', (0,2), (-1,-1), 14),
        ('LEFTPADDING',   (0,0), (-1,-1), 12),
        ('RIGHTPADDING',  (0,0), (-1,-1), 12),
    ]))
    story.append(banner)
    story.append(Spacer(1, 0.5*cm))

    # ── Helper: section header ────────────────────────────────────────────────
    def section_header(text):
        tbl = Table([[Paragraph(text, h1)]], colWidths=[17*cm])
        tbl.setStyle(TableStyle([
            ('BACKGROUND', (0,0), (-1,-1), TEAL),
            ('TOPPADDING',    (0,0), (-1,-1), 6),
            ('BOTTOMPADDING', (0,0), (-1,-1), 6),
            ('LEFTPADDING',   (0,0), (-1,-1), 10),
            ('RIGHTPADDING',  (0,0), (-1,-1), 10),
        ]))
        return tbl

    def bullet_item(text):
        return Paragraph(f"• {text}", bullet)

    def sub_bullet(text):
        return Paragraph(f"  – {text}", S('SubBullet', parent='Normal', fontSize=9,
                         fontName='Helvetica', spaceAfter=2, leading=12,
                         leftIndent=24, firstLineIndent=-10, textColor=BLACK))

    # ═══════════════════════════════════════════════════════════════════════════
    # 1. OVERVIEW
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header("1.  OVERVIEW"))
    story.append(Spacer(1, 0.3*cm))

    overview_data = [
        [Paragraph("Definition", emph),
         Paragraph("Malignant neoplasm of clonal plasma cells infiltrating the bone marrow, secreting a monoclonal immunoglobulin (M protein), and causing skeletal destruction.", body)],
        [Paragraph("Incidence", emph),
         Paragraph("~30,000 new cases/year in the US. Accounts for ~1% of all malignancies and >10% of haematologic malignancies.", body)],
        [Paragraph("Demographics", emph),
         Paragraph("Median age at diagnosis 65–70 years; only 2% &lt;40 years. More common in males. Incidence in Black Americans is ~2× that of White Americans.", body)],
        [Paragraph("Precursor", emph),
         Paragraph("Nearly all cases evolve from MGUS (monoclonal gammopathy of undetermined significance), which progresses at ~1%/year.", body)],
    ]
    overview_tbl = Table(overview_data, colWidths=[3.5*cm, 13.5*cm])
    overview_tbl.setStyle(TableStyle([
        ('VALIGN',       (0,0), (-1,-1), 'TOP'),
        ('ROWBACKGROUNDS',(0,0),(-1,-1), [LTGREY, WHITE]),
        ('TOPPADDING',   (0,0), (-1,-1), 5),
        ('BOTTOMPADDING',(0,0), (-1,-1), 5),
        ('LEFTPADDING',  (0,0), (-1,-1), 6),
        ('RIGHTPADDING', (0,0), (-1,-1), 6),
        ('GRID',         (0,0), (-1,-1), 0.25, MIDGREY),
    ]))
    story.append(overview_tbl)
    story.append(Spacer(1, 0.4*cm))

    # ═══════════════════════════════════════════════════════════════════════════
    # 2. M PROTEIN TYPES
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header("2.  M PROTEIN DISTRIBUTION"))
    story.append(Spacer(1, 0.3*cm))

    mp_data = [
        [Paragraph("M Protein Type", table_hdr), Paragraph("Frequency", table_hdr),
         Paragraph("Light Chain", table_hdr), Paragraph("Frequency", table_hdr)],
        ["IgG", "52%", "Kappa (κ)", "65%"],
        ["IgA", "21%", "Lambda (λ)", "35%"],
        ["Light chain only", "16%", "", ""],
        ["IgD", "2%", "", ""],
        ["Biclonal", "2%", "", ""],
        ["Nonsecretory", "1–3%", "", ""],
    ]
    for row in mp_data[1:]:
        for i in range(len(row)):
            if isinstance(row[i], str):
                row[i] = Paragraph(row[i], table_cell_c)

    mp_tbl = Table(mp_data, colWidths=[4.5*cm, 3.5*cm, 5*cm, 3.5*cm])
    mp_tbl.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), NAVY),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [LTGREY, WHITE]),
        ('GRID',       (0,0), (-1,-1), 0.5, MIDGREY),
        ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
        ('ALIGN',      (0,0), (-1,-1), 'CENTER'),
        ('VALIGN',     (0,0), (-1,-1), 'MIDDLE'),
        ('TOPPADDING', (0,0), (-1,-1), 4),
        ('BOTTOMPADDING',(0,0), (-1,-1), 4),
        ('SPAN',       (2,3), (3,5)),
    ]))
    story.append(mp_tbl)
    story.append(Spacer(1, 0.4*cm))

    # ═══════════════════════════════════════════════════════════════════════════
    # 3. CLINICAL FEATURES – CRAB
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header('3.  CLINICAL FEATURES – "CRAB" CRITERIA'))
    story.append(Spacer(1, 0.3*cm))

    crab_data = [
        [Paragraph("Letter", table_hdr), Paragraph("Feature", table_hdr),
         Paragraph("Mechanism", table_hdr), Paragraph("Key Points", table_hdr)],
        [Paragraph("C", ParagraphStyle('CRed', parent=styles['Normal'], fontSize=14,
                   fontName='Helvetica-Bold', textColor=RED, alignment=TA_CENTER)),
         Paragraph("Hyper-\ncalcaemia", table_cell_c),
         Paragraph("Osteoclast activation → bone resorption", table_cell),
         Paragraph("Confusion, weakness, lethargy, polyuria, constipation", table_cell)],
        [Paragraph("R", ParagraphStyle('CAmb', parent=styles['Normal'], fontSize=14,
                   fontName='Helvetica-Bold', textColor=AMBER, alignment=TA_CENTER)),
         Paragraph("Renal\ninsufficiency", table_cell_c),
         Paragraph("Bence Jones casts, light chain deposition, hypercalcaemia", table_cell),
         Paragraph("Renal failure in up to 50% of patients; leading cause of death", table_cell)],
        [Paragraph("A", ParagraphStyle('CGrn', parent=styles['Normal'], fontSize=14,
                   fontName='Helvetica-Bold', textColor=TEAL, alignment=TA_CENTER)),
         Paragraph("Anaemia", table_cell_c),
         Paragraph("Bone marrow infiltration by plasma cells", table_cell),
         Paragraph("Normocytic normochromic; present in ~75% at diagnosis", table_cell)],
        [Paragraph("B", ParagraphStyle('CNav', parent=styles['Normal'], fontSize=14,
                   fontName='Helvetica-Bold', textColor=NAVY, alignment=TA_CENTER)),
         Paragraph("Bone\nlesions", table_cell_c),
         Paragraph("RANKL/OPG imbalance → osteoclast activation + osteoblast suppression", table_cell),
         Paragraph("Punched-out lytic lesions; pathologic fractures; bone pain", table_cell)],
    ]
    crab_tbl = Table(crab_data, colWidths=[1.5*cm, 3*cm, 6.5*cm, 6*cm])
    crab_tbl.setStyle(TableStyle([
        ('BACKGROUND',   (0,0), (-1,0), NAVY),
        ('ROWBACKGROUNDS',(0,1),(-1,-1), [LTGREY, WHITE, LTGREY, WHITE]),
        ('GRID',         (0,0), (-1,-1), 0.5, MIDGREY),
        ('ALIGN',        (0,0), (1,-1), 'CENTER'),
        ('VALIGN',       (0,0), (-1,-1), 'MIDDLE'),
        ('TOPPADDING',   (0,0), (-1,-1), 5),
        ('BOTTOMPADDING',(0,0), (-1,-1), 5),
        ('LEFTPADDING',  (0,0), (-1,-1), 6),
    ]))
    story.append(crab_tbl)
    story.append(Spacer(1, 0.3*cm))
    story.append(Paragraph(
        "Additional features: recurrent bacterial infections (depressed normal Ig production), "
        "radiculopathy/spinal cord compression (nerve compression by vertebral lesions), "
        "peripheral neuropathy (usually from AL amyloidosis).",
        note))
    story.append(Spacer(1, 0.3*cm))

    # ═══════════════════════════════════════════════════════════════════════════
    # 4. DIAGNOSTIC CRITERIA
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header("4.  DIAGNOSTIC CRITERIA  (IMWG 2014)"))
    story.append(Spacer(1, 0.3*cm))

    story.append(Paragraph(
        "Multiple myeloma is diagnosed when ALL THREE of the following are met:", body))

    criteria = [
        ("1", "≥10% clonal plasma cells on bone marrow biopsy", TEAL),
        ("2", "Serum and/or urine M protein (except nonsecretory myeloma)", TEAL),
        ("3", "One or more myeloma-defining events (MDE):", TEAL),
    ]
    mde_items = [
        "Hypercalcaemia: serum Ca >0.25 mmol/L above upper limit of normal, or >2.75 mmol/L",
        "Renal insufficiency: creatinine clearance <40 mL/min or serum creatinine >177 μmol/L",
        "Anaemia: Hb >20 g/L below lower limit of normal, or Hb <100 g/L",
        "Bone lesions: one or more lytic lesions on skeletal survey, CT, or PET-CT",
        "Clonal BMPC ≥60% (biomarker MDE)",
        "Serum involved/uninvolved free light chain (FLC) ratio ≥100 (biomarker MDE)",
        "MRI: >1 focal lesion ≥5 mm (biomarker MDE)",
    ]

    criteria_data = []
    for num, text, col in criteria:
        criteria_data.append([
            Paragraph(num, ParagraphStyle('Num', parent=styles['Normal'],
                      fontSize=12, fontName='Helvetica-Bold',
                      textColor=WHITE, alignment=TA_CENTER)),
            Paragraph(text, body)
        ])
    criteria_tbl = Table(criteria_data, colWidths=[1*cm, 16*cm])
    criteria_tbl.setStyle(TableStyle([
        ('BACKGROUND',   (0,0), (0,-1), TEAL),
        ('BACKGROUND',   (1,0), (1,-1), LTBLUE),
        ('GRID',         (0,0), (-1,-1), 0.5, MIDGREY),
        ('VALIGN',       (0,0), (-1,-1), 'MIDDLE'),
        ('TOPPADDING',   (0,0), (-1,-1), 6),
        ('BOTTOMPADDING',(0,0), (-1,-1), 6),
        ('LEFTPADDING',  (1,0), (1,-1), 10),
    ]))
    story.append(criteria_tbl)
    story.append(Spacer(1, 0.15*cm))
    story.append(Paragraph("<b>Myeloma-Defining Events (MDE) include:</b>", emph))
    for item in mde_items:
        story.append(bullet_item(item))
    story.append(Spacer(1, 0.2*cm))

    # Differentiation box
    diff_data = [[
        Paragraph("<b>Differentiating from Precursor States</b>", 
                  ParagraphStyle('DiffHdr', parent=styles['Normal'], fontSize=10,
                                 fontName='Helvetica-Bold', textColor=WHITE)),
    ],[
        Paragraph(
            "<b>MGUS:</b> M protein &lt;30 g/L, clonal BMPC &lt;10%, <i>no</i> MDE or amyloidosis<br/>"
            "<b>Smoldering MM:</b> M protein ≥30 g/L or urine M ≥500 mg/24h or BMPC 10–60%, <i>no</i> MDE<br/>"
            "<b>Active MM:</b> BMPC ≥10% (or biopsy-proven plasmacytoma) <i>plus</i> ≥1 MDE",
            ParagraphStyle('DiffBody', parent=styles['Normal'], fontSize=9,
                           fontName='Helvetica', leading=14, textColor=BLACK)
        )
    ]]
    diff_tbl = Table(diff_data, colWidths=[17*cm])
    diff_tbl.setStyle(TableStyle([
        ('BACKGROUND',   (0,0), (-1,0), NAVY),
        ('BACKGROUND',   (0,1), (-1,1), LTBLUE),
        ('TOPPADDING',   (0,0), (-1,-1), 6),
        ('BOTTOMPADDING',(0,0), (-1,-1), 6),
        ('LEFTPADDING',  (0,0), (-1,-1), 10),
        ('RIGHTPADDING', (0,0), (-1,-1), 10),
        ('BOX',          (0,0), (-1,-1), 0.5, TEAL),
    ]))
    story.append(diff_tbl)
    story.append(Spacer(1, 0.4*cm))

    # ═══════════════════════════════════════════════════════════════════════════
    # 5. INVESTIGATIONS
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header("5.  INVESTIGATIONS"))
    story.append(Spacer(1, 0.3*cm))

    inv_data = [
        [Paragraph("Test", table_hdr), Paragraph("Finding / Purpose", table_hdr),
         Paragraph("Sensitivity", table_hdr)],
        [Paragraph("Serum protein electrophoresis (SPEP)", table_cell),
         Paragraph("M protein spike (paraprotein band)", table_cell),
         Paragraph("80%", table_cell_c)],
        [Paragraph("Serum immunofixation", table_cell),
         Paragraph("Confirms M protein type (more sensitive)", table_cell),
         Paragraph("93%", table_cell_c)],
        [Paragraph("SPEP + urine electrophoresis + immunofixation", table_cell),
         Paragraph("Combined approach for M protein detection", table_cell),
         Paragraph("97%", table_cell_c)],
        [Paragraph("Serum free light chain (FLC) assay", table_cell),
         Paragraph("Convenient alternative to urine studies; κ/λ ratio", table_cell),
         Paragraph("High", table_cell_c)],
        [Paragraph("24-hour urine protein electrophoresis", table_cell),
         Paragraph("Quantifies Bence Jones protein (free light chains)", table_cell),
         Paragraph("—", table_cell_c)],
        [Paragraph("Bone marrow biopsy", table_cell),
         Paragraph(">10% plasma cells; flow cytometry (CD38+, CD138+, CD45−, CD56+); κ/λ clonality", table_cell),
         Paragraph("Definitive", table_cell_c)],
        [Paragraph("Full blood count", table_cell),
         Paragraph("Normocytic normochromic anaemia; leukopenia; thrombocytopenia", table_cell),
         Paragraph("—", table_cell_c)],
        [Paragraph("Biochemistry panel", table_cell),
         Paragraph("Calcium, creatinine, uric acid, LDH, β2-microglobulin, albumin", table_cell),
         Paragraph("—", table_cell_c)],
        [Paragraph("Imaging: skeletal survey (X-ray)", table_cell),
         Paragraph("Punched-out lytic lesions (vertebrae, ribs, skull, pelvis, femur)", table_cell),
         Paragraph("First-line", table_cell_c)],
        [Paragraph("Imaging: whole-body low-dose CT", table_cell),
         Paragraph("More sensitive than X-ray; preferred for bone assessment", table_cell),
         Paragraph("Preferred", table_cell_c)],
        [Paragraph("PET-CT / MRI", table_cell),
         Paragraph("Extramedullary disease, focal bone marrow lesions, treatment response", table_cell),
         Paragraph("Highest", table_cell_c)],
    ]
    inv_tbl = Table(inv_data, colWidths=[5*cm, 9*cm, 3*cm])
    inv_tbl.setStyle(TableStyle([
        ('BACKGROUND',    (0,0), (-1,0), NAVY),
        ('ROWBACKGROUNDS',(0,1),(-1,-1), [LTGREY, WHITE]),
        ('GRID',          (0,0), (-1,-1), 0.5, MIDGREY),
        ('VALIGN',        (0,0), (-1,-1), 'TOP'),
        ('TOPPADDING',    (0,0), (-1,-1), 4),
        ('BOTTOMPADDING', (0,0), (-1,-1), 4),
        ('LEFTPADDING',   (0,0), (-1,-1), 6),
    ]))
    story.append(inv_tbl)
    story.append(Spacer(1, 0.4*cm))

    # ═══════════════════════════════════════════════════════════════════════════
    # PAGE BREAK before staging
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(PageBreak())

    # ═══════════════════════════════════════════════════════════════════════════
    # 6. STAGING – R-ISS
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header("6.  STAGING  –  REVISED INTERNATIONAL STAGING SYSTEM (R-ISS)"))
    story.append(Spacer(1, 0.3*cm))

    riss_data = [
        [Paragraph("Stage", table_hdr), Paragraph("Criteria", table_hdr),
         Paragraph("Median OS", table_hdr)],
        [Paragraph("I", ParagraphStyle('S1', parent=styles['Normal'], fontSize=13,
                   fontName='Helvetica-Bold', textColor=GREEN, alignment=TA_CENTER)),
         Paragraph(
             "• Serum β2-microglobulin &lt;3.5 mg/L<br/>"
             "• Serum albumin ≥35 g/L<br/>"
             "• Standard-risk cytogenetics<br/>"
             "• Normal LDH",
             table_cell),
         Paragraph("~62 months", table_cell_c)],
        [Paragraph("II", ParagraphStyle('S2', parent=styles['Normal'], fontSize=13,
                   fontName='Helvetica-Bold', textColor=AMBER, alignment=TA_CENTER)),
         Paragraph(
             "• Not Stage I or III<br/>"
             "(intermediate — any combination not meeting Stage I or III)",
             table_cell),
         Paragraph("~44 months", table_cell_c)],
        [Paragraph("III", ParagraphStyle('S3', parent=styles['Normal'], fontSize=13,
                   fontName='Helvetica-Bold', textColor=RED, alignment=TA_CENTER)),
         Paragraph(
             "• Serum β2-microglobulin ≥5.5 mg/L<br/>"
             "• PLUS high-risk cytogenetics: del(17p), t(4;14), or t(14;16)<br/>"
             "• OR elevated LDH",
             table_cell),
         Paragraph("~29 months", table_cell_c)],
    ]
    riss_tbl = Table(riss_data, colWidths=[2*cm, 11.5*cm, 3.5*cm])
    riss_tbl.setStyle(TableStyle([
        ('BACKGROUND',    (0,0), (-1,0), NAVY),
        ('BACKGROUND',    (0,1), (-1,1), HexColor("#e8f8ee")),
        ('BACKGROUND',    (0,2), (-1,2), HexColor("#fff8e8")),
        ('BACKGROUND',    (0,3), (-1,3), HexColor("#fdecea")),
        ('GRID',          (0,0), (-1,-1), 0.5, MIDGREY),
        ('ALIGN',         (0,0), (0,-1), 'CENTER'),
        ('ALIGN',         (2,0), (2,-1), 'CENTER'),
        ('VALIGN',        (0,0), (-1,-1), 'MIDDLE'),
        ('TOPPADDING',    (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('LEFTPADDING',   (1,0), (1,-1), 10),
    ]))
    story.append(riss_tbl)
    story.append(Spacer(1, 0.2*cm))
    story.append(Paragraph(
        "OS = overall survival with modern treatment. Cytogenetics assessed by FISH.",
        note))
    story.append(Spacer(1, 0.4*cm))

    # ═══════════════════════════════════════════════════════════════════════════
    # 7. CYTOGENETICS
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header("7.  CYTOGENETIC ABNORMALITIES"))
    story.append(Spacer(1, 0.3*cm))

    cyto_data = [
        [Paragraph("Abnormality", table_hdr), Paragraph("Type", table_hdr),
         Paragraph("Frequency", table_hdr), Paragraph("Prognosis", table_hdr)],
        [Paragraph("Hyperdiploidy (trisomies)", table_cell), Paragraph("Primary", table_cell_c),
         Paragraph("~40%", table_cell_c), Paragraph("Standard risk", table_cell_c)],
        [Paragraph("IgH translocations (any)", table_cell), Paragraph("Primary", table_cell_c),
         Paragraph("~40%", table_cell_c), Paragraph("Varies by partner", table_cell_c)],
        [Paragraph("t(11;14) – CCND1", table_cell), Paragraph("Primary", table_cell_c),
         Paragraph("~15%", table_cell_c), Paragraph("Standard risk", table_cell_c)],
        [Paragraph("t(4;14) – FGFR3/MMSET", table_cell), Paragraph("Primary", table_cell_c),
         Paragraph("~15%", table_cell_c),
         Paragraph("HIGH RISK", ParagraphStyle('HR', parent=styles['Normal'], fontSize=8.5,
                   fontName='Helvetica-Bold', textColor=RED, alignment=TA_CENTER))],
        [Paragraph("t(14;16) – MAF", table_cell), Paragraph("Primary", table_cell_c),
         Paragraph("~5%", table_cell_c),
         Paragraph("HIGH RISK", ParagraphStyle('HR2', parent=styles['Normal'], fontSize=8.5,
                   fontName='Helvetica-Bold', textColor=RED, alignment=TA_CENTER))],
        [Paragraph("del(17p) – TP53", table_cell), Paragraph("Secondary", table_cell_c),
         Paragraph("~10%", table_cell_c),
         Paragraph("HIGH RISK", ParagraphStyle('HR3', parent=styles['Normal'], fontSize=8.5,
                   fontName='Helvetica-Bold', textColor=RED, alignment=TA_CENTER))],
        [Paragraph("1q gain/amplification", table_cell), Paragraph("Secondary", table_cell_c),
         Paragraph("~40%", table_cell_c),
         Paragraph("HIGH RISK", ParagraphStyle('HR4', parent=styles['Normal'], fontSize=8.5,
                   fontName='Helvetica-Bold', textColor=RED, alignment=TA_CENTER))],
        [Paragraph("MYC translocation", table_cell), Paragraph("Late/Secondary", table_cell_c),
         Paragraph("Variable", table_cell_c), Paragraph("Aggressive disease", table_cell_c)],
    ]
    cyto_tbl = Table(cyto_data, colWidths=[5.5*cm, 3*cm, 3*cm, 5.5*cm])
    cyto_tbl.setStyle(TableStyle([
        ('BACKGROUND',    (0,0), (-1,0), NAVY),
        ('ROWBACKGROUNDS',(0,1),(-1,-1), [LTGREY, WHITE]),
        ('GRID',          (0,0), (-1,-1), 0.5, MIDGREY),
        ('ALIGN',         (1,0), (-1,-1), 'CENTER'),
        ('VALIGN',        (0,0), (-1,-1), 'MIDDLE'),
        ('TOPPADDING',    (0,0), (-1,-1), 4),
        ('BOTTOMPADDING', (0,0), (-1,-1), 4),
        ('LEFTPADDING',   (0,0), (-1,-1), 6),
    ]))
    story.append(cyto_tbl)
    story.append(Spacer(1, 0.4*cm))

    # ═══════════════════════════════════════════════════════════════════════════
    # 8. BONE MARROW FINDINGS
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header("8.  BONE MARROW &amp; HISTOLOGY FINDINGS"))
    story.append(Spacer(1, 0.3*cm))

    bm_blocks = [
        ("Plasma cell percentage",
         ">10% of nucleated cells in 96% of patients. May be focal — repeat biopsy may be needed."),
        ("Morphology",
         "Plasma cells typically eccentric nucleus with 'clock-face' chromatin and basophilic cytoplasm. "
         "Myeloma cells often show prominent nucleoli or cytoplasmic inclusions (Russell bodies — "
         "eosinophilic immunoglobulin aggregates)."),
        ("Immunophenotype",
         "Cytoplasmic Ig+, CD38+, CD138+, CD45−, CD56+. Only 20% express CD20. "
         "Clonality confirmed by abnormal κ/λ ratio (>4:1 kappa or <1:2 lambda)."),
        ("Skeletal lesions",
         "Punched-out lytic defects 1–4 cm. Most commonly: vertebral column, ribs, skull, pelvis, femur, "
         "clavicle, scapula. Arise in medullary cavity and erode cortex."),
        ("Myeloma kidney",
         "Bence Jones protein casts in distal tubules and collecting ducts, surrounded by multinucleate "
         "giant cells. Adjacent epithelial cells may be necrotic. AL amyloidosis may also affect glomeruli."),
    ]
    for label, text in bm_blocks:
        row_data = [[Paragraph(f"<b>{label}</b>", 
                               ParagraphStyle('BML', parent=styles['Normal'], fontSize=9.5,
                               fontName='Helvetica-Bold', textColor=TEAL, leading=13)),
                     Paragraph(text, body)]]
        rt = Table(row_data, colWidths=[4*cm, 13*cm])
        rt.setStyle(TableStyle([
            ('VALIGN',       (0,0), (-1,-1), 'TOP'),
            ('TOPPADDING',   (0,0), (-1,-1), 4),
            ('BOTTOMPADDING',(0,0), (-1,-1), 4),
            ('LEFTPADDING',  (0,0), (-1,-1), 6),
            ('LINEBELOW',    (0,0), (-1,-1), 0.25, MIDGREY),
        ]))
        story.append(rt)
    story.append(Spacer(1, 0.4*cm))

    # ═══════════════════════════════════════════════════════════════════════════
    # 9. PROGNOSIS
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header("9.  PROGNOSIS"))
    story.append(Spacer(1, 0.3*cm))

    prog_data = [
        [Paragraph("Scenario", table_hdr), Paragraph("Median Survival", table_hdr)],
        [Paragraph("Active multiple myeloma (modern treatment)", table_cell),
         Paragraph("~5 years (significantly better in younger, standard-risk patients)", table_cell)],
        [Paragraph("Multiple bony lesions — untreated", table_cell),
         Paragraph("6–12 months", table_cell)],
        [Paragraph("Smoldering myeloma", table_cell),
         Paragraph("May be asymptomatic for many years; ~1%/year progression risk", table_cell)],
        [Paragraph("High-risk cytogenetics (del17p, t(4;14), t(14;16))", table_cell),
         Paragraph("Significantly shorter — R-ISS III median ~29 months", table_cell)],
    ]
    prog_tbl = Table(prog_data, colWidths=[9*cm, 8*cm])
    prog_tbl.setStyle(TableStyle([
        ('BACKGROUND',    (0,0), (-1,0), NAVY),
        ('ROWBACKGROUNDS',(0,1),(-1,-1), [LTGREY, WHITE]),
        ('GRID',          (0,0), (-1,-1), 0.5, MIDGREY),
        ('VALIGN',        (0,0), (-1,-1), 'TOP'),
        ('TOPPADDING',    (0,0), (-1,-1), 5),
        ('BOTTOMPADDING', (0,0), (-1,-1), 5),
        ('LEFTPADDING',   (0,0), (-1,-1), 6),
    ]))
    story.append(prog_tbl)
    story.append(Spacer(1, 0.2*cm))

    cause_data = [[
        Paragraph("Leading causes of death: (1) Bacterial infections — due to suppressed normal antibody production  |  (2) Renal failure — Bence Jones proteinuria and cast nephropathy",
                  ParagraphStyle('CauseBox', parent=styles['Normal'], fontSize=9,
                                 fontName='Helvetica-Bold', textColor=NAVY, leading=14))
    ]]
    cause_tbl = Table(cause_data, colWidths=[17*cm])
    cause_tbl.setStyle(TableStyle([
        ('BACKGROUND',   (0,0), (-1,-1), HexColor("#fdecea")),
        ('BOX',          (0,0), (-1,-1), 1, RED),
        ('TOPPADDING',   (0,0), (-1,-1), 8),
        ('BOTTOMPADDING',(0,0), (-1,-1), 8),
        ('LEFTPADDING',  (0,0), (-1,-1), 10),
    ]))
    story.append(cause_tbl)
    story.append(Spacer(1, 0.4*cm))

    # ═══════════════════════════════════════════════════════════════════════════
    # 10. QUICK REFERENCE
    # ═══════════════════════════════════════════════════════════════════════════
    story.append(section_header("10.  QUICK DIAGNOSTIC APPROACH"))
    story.append(Spacer(1, 0.3*cm))

    steps = [
        ("Step 1", "Suspect myeloma",
         "Unexplained anaemia, bone pain/lytic lesions, hypercalcaemia, renal impairment, "
         "recurrent infections, or elevated total protein in patient >50 years"),
        ("Step 2", "Initial investigations",
         "FBC, U&E, calcium, LDH, albumin; SPEP + serum immunofixation; serum FLC ratio; "
         "urine electrophoresis + immunofixation; skeletal survey / whole-body CT"),
        ("Step 3", "Bone marrow examination",
         "Trephine biopsy + aspirate; flow cytometry for immunophenotype; FISH for cytogenetics"),
        ("Step 4", "Apply diagnostic criteria",
         "BMPC ≥10% + M protein + ≥1 MDE = active MM. If criteria not fully met, "
         "consider smoldering MM or MGUS"),
        ("Step 5", "Stage with R-ISS",
         "β2-microglobulin, albumin, LDH, FISH cytogenetics → Stage I / II / III"),
        ("Step 6", "Assess transplant eligibility",
         "Age, performance status, comorbidities → determines induction regimen and ASCT suitability"),
    ]
    for step, title, detail in steps:
        sd = [[
            Paragraph(step, ParagraphStyle('StepNum', parent=styles['Normal'], fontSize=9,
                      fontName='Helvetica-Bold', textColor=WHITE, alignment=TA_CENTER)),
            Paragraph(f"<b>{title}</b><br/>{detail}",
                      ParagraphStyle('StepBody', parent=styles['Normal'], fontSize=9,
                                     fontName='Helvetica', leading=13, textColor=BLACK))
        ]]
        st = Table(sd, colWidths=[2*cm, 15*cm])
        st.setStyle(TableStyle([
            ('BACKGROUND',   (0,0), (0,-1), TEAL),
            ('BACKGROUND',   (1,0), (1,-1), LTBLUE),
            ('VALIGN',       (0,0), (-1,-1), 'MIDDLE'),
            ('TOPPADDING',   (0,0), (-1,-1), 6),
            ('BOTTOMPADDING',(0,0), (-1,-1), 6),
            ('LEFTPADDING',  (1,0), (1,-1), 10),
            ('LINEBELOW',    (0,0), (-1,-1), 0.5, WHITE),
        ]))
        story.append(st)
    story.append(Spacer(1, 0.5*cm))

    # ── Footer note ───────────────────────────────────────────────────────────
    story.append(HRFlowable(width="100%", thickness=0.5, color=MIDGREY))
    story.append(Spacer(1, 0.15*cm))
    story.append(Paragraph(
        "Sources: Goldman-Cecil Medicine International Edition (2022), pp. 1977–1982  |  "
        "Robbins &amp; Kumar Basic Pathology 10e, pp. 420–423  |  "
        "IMWG Diagnostic Criteria 2014 (Rajkumar et al., Lancet Oncology)  |  "
        "Revised ISS (Palumbo et al., JCO 2015)  |  "
        "Prepared using Orris Medical Knowledge System  –  June 2026",
        ParagraphStyle('Footer', parent=styles['Normal'], fontSize=7.5,
                       fontName='Helvetica-Oblique', textColor=HexColor("#777777"),
                       alignment=TA_CENTER, leading=11)
    ))

    doc.build(story)
    print(f"PDF written to {OUTPUT}")

make_doc()

Running Command

cd /home/daytona/workspace/myeloma-summary && python build_pdf.py 2>&1

Running Command

ls -lh /home/daytona/workspace/myeloma-summary/

Your PDF is ready:

Multiple Myeloma Diagnosis Staging

PDF Document · PDF

The document is a 2-page A4 reference summary covering 10 sections, formatted for easy reading and sharing:

Section	Contents
1. Overview	Definition, incidence, demographics, MGUS precursor
2. M Protein Distribution	IgG/IgA/light chain breakdown with frequencies
3. CRAB Criteria	Colour-coded table with mechanism and key points per feature
4. Diagnostic Criteria	IMWG 2014 criteria with full MDE list; differentiation from MGUS/smoldering MM
5. Investigations	All key tests with sensitivity — SPEP, immunofixation, FLC, bone marrow biopsy, imaging
6. R-ISS Staging	Stages I/II/III with full criteria and median OS figures
7. Cytogenetics	Primary vs secondary abnormalities with risk classification
8. Bone Marrow Findings	Plasma cell morphology, immunophenotype, myeloma kidney
9. Prognosis	Survival by scenario; leading causes of death highlighted in a red alert box
10. Quick Diagnostic Approach	Step-by-step 6-step pathway from suspicion to staging

Sources cited at the bottom: Goldman-Cecil Medicine, Robbins Pathology, IMWG 2014, and R-ISS (Palumbo et al. 2015).

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